Selecting RL algorithms is normally performed by expert knowledge or using approaches explained in previous sections. Instead of using existing RL algorithms that perhaps each of them works well on certain type of problems, a model is introduced in [1]} that can learn a RL algorithm. Specifically, a distribution \(D\)  over MDPs is defined and a RL algorithm is learned in the sense that it performs well on the MDPs drawn from \(D\) . During solving MDP with reinforcement learning, an RNN is trained that its inputs are states, actions and rewards of the MDP and the output is the policy. Therefore, a recurrent neural network works as a RL algorithm.
